International Journal of Engineering Inventions (IJEI)


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International Journal of Engineering Inventions (IJEI)

  1. 1. International Journal of Engineering InventionsISSN: 2278-7461, www.ijeijournal.comVolume 1, Issue 6(October2012) PP: 49-54 Optimum Power Dispatch Problems: An Overview F. R. Pazheri1, 2, N. H. Malik2, M. F. Othman3 and M. Babar2 1 Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia. 2 Saudi Aramco Chair in Electrical Power, EE Department, College of Engineering, King Saud University, P. O. Box 800, Riyadh 11421, Saudi Arabia.3 Centre for Artificial Intelligence & Robotics, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Jalan Semarak 54100, Kuala Lumpur, Malaysia.Abstract––An efficient operating policy for committed units (CU) in order to meet the load demand is very important forpower system operators. Optimum Power Dispatch (OPD) problems help to find such suitable operating policies for CUswithout violating system and unit constraints. Minimization of fuel cost or reduction of amount of pollutants emission oroptimization of both fuel cost and emission are the objectives of OPD problems. Based on the objectives, OPD problemsare classifieds and are briefly discussed in this paper. The OPD problem of hybrid power system with and without solarand wind energies is considered and this paper illustrates the OPD with MATLAB simulations.Keywords––economic dispatch, environmental friendly dispatch, multi-objective, optimization, renewable energy. I. INTRODUCTION Optimum power dispatch problem is to find the optimum scheduling for committed units in order to meet the loaddemand while satisfying all unit and system constraints [1-4]. World consumption of energy was about 84 million barrels perday (MBD) in 2008. According to International Energy Agency it will reach 113 MBD by year 2030. According toExecutive Director for Exploration & Production for Royal Dutch, the world energy demand could double by 2050 [5]. Theenergy consumption of developing countries has increased more than four times over the past three decades. The annualdemand growth of developing countries is 2.7 % while that of industrialized countries is 1.2 % [6] The existing energy production is not clean. About 63% of world electricity is obtained by burning fossil fuels;40% of which is from coal-fired electric power stations. Most of the coal-fired power stations were built two decades beforeand emit 80-85% of NOx generated by utilities. Some older power plants operate with pollution rates of up to 70 to 100 timesgreater than the new plants [7-10]. Due to the increased cost and dwindling supplies of fossil fuels, awareness on carbonfootprint and effect of global warming, the utilities have been forced to use renewable energy sources in hybrid powersystems and to modify their operation strategies in order to reduce fuel cost, fuel demand, and atmospheric emissionpollutions by power plants. OPD is the suitable tool for the same. Economic Dispatch (ED), Environmental FriendlyDispatch (EFD) and Economical and Environmental Dispatch (EED) are some of the important OPD problems that arediscussed in this paper. Minimizing the fuel cost is the objective of ED problems while the minimization of pollutants emission is theobjective of EFD [11, 12]. The EED problem minimize both fuels cost and amount of emissions. EED distributes active andrenewable production among the available power stations to meet the minimization of both fuel cost and pollutant emissionssimultaneously [13, 14]. However, this distribution in case of ED is only for minimizing fuel cost while in EFD; it is only forreducing the amount of emissions. ED and EFD are treated as single objective problems while EED is treated as a multi-objective problem [1, 12]. Renewable energy resources depend on the climate data such as the wind speed, solar radiation, and temperature.The uncertainty and the variation of the renewable resources create issues in OPD problems. Different methodologies wereillustrated in several articles to overcome these issues [11-17]. One of the methods is to treat renewable power as a negativeload and formulate demand equation on this basis [11, 16, 18]. The amount of dispatching active and renewable power iscalculated based on the data conveyed by the Environmental Information Systems and Load Dispatch Centers, using anycommercially available software package [8]. In this paper, the OPD is formulated with and without renewable sources. Theobjective functions and constraints of OPD problems are described next. II. OBJECTIVE FUNCTIONS AND CONSTRAINTSThe main objective of ED is to minimize fuel cost. Hence, the objective function is fuel cost function.The fuel cost function Ff (Pgi) in $/h is represented by a quadratic equation of the type as;  Ff Pgi    a i  b i Pgi  c i Pgi  Ng 2 (1) i 1 49
  2. 2. Optimum Power Dispatch Problems: An OverviewIn (1), ai, bi and ci are the appropriate cost coefficients for individual generating units, P gi is the real power output of the ithgenerator and Ng is the number of total generators. Main emissions in thermal power plants are SO2 and NOx. The emission of SO2 depends on fuel consumption andemission quantity has the same form as the fuel cost function. The emission of NO x is related to many factors such as thetemperature of the boiler and content of the air. The objective function of EFD is emission function F e(Pgi) and can beexpressed in ton/h as;  Fe Pgi    α i  β i Pgi  γ i Pgi  λ i e  Ng 2 δi Pgi (2) i 1Here αi, βi, γi, λi and δi are emission coefficients of the ith generating unit. The EED problem optimizes both fuel cost andemissions simultaneously. Hence, its objective functions contain both fuel cost and emission functions. The power extracted from the renewable source varies and can be considered as a variable load. Therefore solar power P sand wind power Pw are deducted from the total demand P  . The net actual demand is thus expressed as; t D PD  PD  Ps  Pw  a t (3)The main constraints of OPD can be formulated as follows: The total power generation must cover the actual demand and the power loss (P L) in transmission lines so as to ensurepower balance, i.e. Ng PD  PL   Pgi  0 a (4) i1 min max The generated real power of ith unit is restricted by the lower limit Pgi and the upper limit Pgi i. e. Pgi  Pgi  Pgi , i=1, 2, ….., Ng min max (5) Active power loss of the transmission line is positive, i.e., PL  0 (6) The dispatched amount of renewable power is limited to some part (x) of the total actual demand, i.e. Ps  Pw   xPD a (7) III. OPD FORMULATIONThe optimization of OPD problems based on their objectives and constraints are formulated as follows;The optimization of ED can be summarized as; Minimize [Ff (Pgi)] Subjected to the constraints given in (4) to (7).EFD can be summarized as; Minimize [Fe (Pgi)] Subjected to the constraints given in (4) to (7).EED optimization can be summarized as; Minimize [Ff (Pgi), Fe (Pgi)] Subjected to the constraints given in (4) to (7). A comparative analysis of OPD problems are carried out using MATLAB simulations and are discussed next. 50
  3. 3. Optimum Power Dispatch Problems: An Overview IV. RESULTS AND DISCUSSIONS The simulations of OPD problems with given constraints were performed using MATLAB. The values of the fueland emission coefficients which were used for illustration are given in Tables 1. Optimization is done in presence of withand without renewable energy sources. The lower and upper limits of generated power of each generator are given as; 0.05 pu  Pgi  1.5 pu ; i=1, 2, ….., 6 (8). TABLE 1: Generator Cost and Emission Coefficients [1] a b c α β γ λ δ Pg1 10 200 100 4.091 -5.554 6.490 2x10-4 2.857 Pg2 10 150 120 2.543 -6.047 5.638 5x10-4 3.333 Pg3 20 180 40 4.258 -5.094 4.586 1x10-6 8.000 Pg4 10 100 60 5.326 -3.55 3.380 2x10-3 2.000 Pg5 20 180 40 4.258 -5.094 4.586 1x10-6 8.000 Pg6 10 150 100 6.131 -5.555 5.151 1x10-5 6.667 The variation of fuel cost for a specified power demand is shown in Fig. 1. The cost without the use of renewablepower (CN) is always more than that of with renewable power (CR). The percentage reduction in the fuel cost %ΔC withpower demand is shown in Fig. 2. More than 30% of fuel cost can be reduced while dispatching 3 pu power demand inpresence of renewable power. 800 CN 700 CR 600 500 Cost ($/h) 400 300 200 100 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Power Demand (pu) Fig. 1 Variations of Fuel Cost with Power Demand 35 % C = (1-CR/CN)x100 30 25 % C 20 15 10 0.5 1 1.5 2 2.5 3 3.5 Power Demand (pu) Fig.2 Variation of %ΔC with Demand The variation of pollutants emission for a specified power demand is shown in Fig. 3. If the demand is high thenthe emission without the use renewable power (EN) is more than that obtained with the use renewable power (ER). At lowpower demand the dispatched power from renewable source is negligible. The percentage reduction in the fuel cost %ΔEwith power demand is shown in Fig. 4. 51
  4. 4. Optimum Power Dispatch Problems: An Overview 0.25 EN ER 0.2 Emission (ton/h) 0.15 0.1 0.05 0 0 1 2 3 4 5 Power Demand (pu) Fig. 3 Variations of Emissions with Power Demand 18 % E=(1-ER/EN)x100 16 14 12 10 % E 8 6 4 2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Power Demand (pu) Fig. 4 Variation of %ΔE with Demand The variation of fuel cost and emissions with power demand is shown in Fig. 5. With increase in power demandthe cost is always less for EED when renewable source is used. However, the amount of emissions is slightly higher for lowdemand if renewable sources are used, but it is lesser for higher demand. Te percentage reductions of both fuel cost andamount of emissions are show in Fig.6. 1000 CN CR Cost($/h) 500 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0.4 Emission(ton/h) EN ER 0.3 0.2 0.1 0 01.5 0.5 2 2.5 1 3 3.5 4 4.5 Power Deman (pu) Fig. 5 Variations of Fuel Cost and Emissions with Power Demand 52
  5. 5. Optimum Power Dispatch Problems: An Overview Fig.6. Variation of %ΔC and %ΔE with Demand The values of CN and CR obtained by ED optimization are slightly less than the corresponding values obtained byEED optimization. Similarly, Comparing EFD and EED, less values of E N and ER are obtained from EFD optimization. Inother words, best saving of fuel cost only obtained with ED optimization while best reduction in emission obtained by EFDoptimization. However, the optimum values of both fuel cost and emission are achieved by minimizing EED problems. V. CONCLUSION OPD problems are formulated in this paper for a hybrid system which includes thermal generating units as well assolar and wind based generations. Analyses were carried out using MATLAB simulation for each OPD problem with andwithout the use of renewable sources. From the analysis it is clear that the efficient use of renewable power can help toreduce both fuel cost and the amount of pollutants emissions.ACKNOWLEDGEMENTS The authors would like to acknowledge Research Center, King Saud University and CAIRO, Universiti TeknologiMalaysia, for supporting this work. REFERENCES 1. Abido, M. A. A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electric Power Systems Research, 2003, 65, pp. 71-81. 2. Pazheri, F.R.; Othman, M. F.; Malik, N.H.; Al-Arainy, A.A. Optimization of pollution emission in power dispatch including renewable energy and energy storage. Research Journal of Applied Sciences, Engineering and Technology, 2012, 4(23), pp. 5149-5156. 3. Saadat, H. Power system analysis, 2 ed. 2002, McGraw-Hill, USA. 4. Wood, A J.; Wollenberg, B. F. Power generation operation and control, 1984, John Wiley & Sons. 5. Fossil fuels. [online], available at: 6. /Fossil_Fuels.html. 7. Mohammadi, V.; Nejad, N. G. Estimation of energy demand in some developing countries; Dynamic panel approach. J. Basic Appl. Sci. Res., 2012, 2(1), pp. 222-227. 8. Palanichamy, C.; Babu, N. S. Day-night weather-based economic power dispatch. IEEE Transactions on Power Systems, 2002, 17, pp. 469-475. 9. Rahman, S; de Castro, A. Environmental impacts of electricity generation: a global perspective. IEEE Transactions on Energy Conversion, 10, pp. 307-314, 1995.10. Zatirostami, A. Air pollution analysis resulted from energy part. J. Basic Appl. Sci. Res., 2011, 1(7), pp. 661-666.11. Uzoije, A. P.; Uzoigwe, L. O.;Kamalu, C. I. O. Activated orange meso-carp carbon (AOMC); An acceptable remediation techniques for crude oil pollution effect. Research Journal of Applied Sciences, Engineering and Technology, 2012, 4(1), pp. 51-58.12. Brini, S.; Abdallah, H. H.; Ouali, A. Economic dispatch for power system include wind and solar thermal energy. Leonardo Journal of Sciences, 2009, pp. 204-220.13. Pazheri, F. R.; Othman, M. F.; Malik, N. H.; Al-Arainy, A.A. Environmental friendly power dispatch in Saudi Arabia with renewable energy and energy storage. In the Proceedings of the Forth International conference on Intelligent and Advanced Systems, 2012, pp: 1-6.14. Chen, Y. M.; Wang, W. S. A particle swarm approach to solve environmental/economic dispatch problem. International Journal of Industrial Engineering Computations, 2010, pp. 157-172. 53
  6. 6. Optimum Power Dispatch Problems: An Overview15. Pazheri, F. R.; Othman, M. F.; Malik, N. H.; Safoora, O. K. Economic and environmental dispatch at high potential renewable area with renewable storage. International Journal of Environmental Science and Development, 2012, 3(2), pp. 177-182.16. Al-Awami, A. T.; Sortomme, E.; El-Sharkawi, M. A. Optimizing economic/environmental dispatch with wind and thermal units. Power & Energy Society General Meeting, 2009, pp. 1-6.17. Le, X.; Ilic, M. D. Model predictive economic/environmental dispatch of power systems with intermittent resources. Power & Energy Society General Meeting, 2009, pp. 1-6.18. Po-Hung Chen; Cheng-Chien Kuo, Economic-emission load dispatch by refined particle swarm optimization and interactive bi-objective programming. International Review of Electrical Engineering (IREE), 2011, 6(5), pp. 2584-2595.19. Anderson D.; Leach, M. Harvesting and redistributing renewable energy: on the role of gas and electricity grids to overcome intermittency through the generation and storage of hydrogen. Energy Policy, 32, 2004, pp. 1603-1614. 54